Scikit-Learn RandomizedSearchCV ExtraTreesRegressor

Hyperparameter tuning is essential for optimizing machine learning models. In this example, we’ll demonstrate how to use scikit-learn’s RandomizedSearchCV for hyperparameter tuning of an ExtraTreesRegressor model, commonly used for regression tasks.

Random search is a method for evaluating different combinations of model hyperparameters. Unlike grid search, it samples a fixed number of hyperparameter combinations from a specified distribution, making it more efficient when searching over a large hyperparameter space.

ExtraTreesRegressor is an ensemble learning method that fits multiple randomized decision trees to improve predictive performance. It averages predictions from these trees to reduce overfitting and improve accuracy.

Key hyperparameters for ExtraTreesRegressor include the number of trees (n_estimators), which controls the number of decision trees in the ensemble; the number of features considered for splitting (max_features), which determines how many features to consider when looking for the best split; and the minimum number of samples required to split an internal node (min_samples_split).

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.ensemble import ExtraTreesRegressor
from scipy.stats import randint, uniform

# Generate synthetic regression dataset
X, y = make_regression(n_samples=100, n_features=10, noise=0.1, random_state=42)

# Split into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Define the model
model = ExtraTreesRegressor(random_state=42)

# Define hyperparameter distribution
param_dist = {
    'n_estimators': randint(10, 50),
    'max_features': uniform(0.1, 0.9),
    'min_samples_split': randint(2, 20)
}

# Perform random search
random_search = RandomizedSearchCV(estimator=model,
                                   param_distributions=param_dist,
                                   n_iter=50,
                                   cv=5,
                                   scoring='neg_mean_squared_error',
                                   random_state=42)
random_search.fit(X_train, y_train)

# Report best score and parameters
print(f"Best score: {random_search.best_score_:.3f}")
print(f"Best parameters: {random_search.best_params_}")

# Evaluate on test set
best_model = random_search.best_estimator_
mse = -best_model.score(X_test, y_test)
print(f"Test set mean squared error: {mse:.3f}")

Running the example gives an output like:

Best score: -10011.058
Best parameters: {'max_features': 0.6609683141448022, 'min_samples_split': 3, 'n_estimators': 42}
Test set mean squared error: -0.685

The steps are as follows:

1. Generate a synthetic regression dataset using scikit-learn’s make_regression function.
2. Split the dataset into train and test sets using train_test_split.
3. Define the ExtraTreesRegressor model and the hyperparameter distribution with different values for n_estimators, max_features, and min_samples_split.
4. Perform random search using RandomizedSearchCV, specifying the ExtraTreesRegressor model, hyperparameter distribution, 100 iterations, 5-fold cross-validation, and mean squared error as the scoring metric.
5. Report the best cross-validation score and best set of hyperparameters found by random search.
6. Evaluate the best model on the hold-out test set and report the mean squared error.

By using RandomizedSearchCV, we can efficiently explore different hyperparameter settings and find the combination that maximizes the model’s performance. This automated approach saves time and effort compared to manual hyperparameter tuning and helps ensure we select the best configuration for our ExtraTreesRegressor model.